Background

Early diagnosis of Parkinson’s disease (PD) enables timely treatment of patients and helps control the course of the disease. An efficient and reliable approach is therefore needed to develop for improving the clinical ability to diagnose this disease.

New Method

We proposed a two-layer stacking ensemble learning framework with fusing multi-modal features in this study, for accurately identifying early PD with healthy control (HC). To begin with, we investigated relative importance of multi-modal neuroimaging (T1 weighted image (T1WI), diffusion tensor imaging (DTI)) and early clinical assessment to classify PD and HC. Next, a two-layer stacking ensemble framework was proposed: at the first layer, we evaluated advantages of these four base classifiers: support vector machine (SVM), random forests (RF), K-nearest neighbor (KNN) and artificial neural network (ANN); at the second layer, a logistic regression (LR) classifier was applied to classify PD. The performance of the proposed model was evaluated by comparing with traditional ensemble models.

Results

The proposed method performed an accuracy of 96.88 %, a precision of 100 %, a recall of 95 % and a $F_1$ score of 97.44 % respectively for identifying PD and HC.

Comparison with Existing Method

The classification results showed that the proposed model achieved a superior performance in comparison with traditional ensemble models.

Conclusion

The stacking ensemble model with efficiently and effectively integrate multiple base classifiers performed higher accuracy than each single traditional model. The method developed in this study provided a novel strategy to enhance the accuracy of diagnosis and early detection of PD.

中文翻译：

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基于多峰特征和叠加集成学习的帕金森病分类

背景

帕金森氏病（PD）的早期诊断可以及时治疗患者，并有助于控制疾病的进程。因此需要开发一种有效且可靠的方法来改善诊断该疾病的临床能力。

新方法

在本研究中，我们提出了一个具有融合多模式特征的两层堆叠集成学习框架，用于通过健康控制（HC）准确识别早期PD。首先，我们调查了多模式神经影像学（T1加权图像（T1WI），弥散张量成像（DTI））和早期临床评估对PD和HC进行分类的相对重要性。接下来，提出了一个两层的堆栈集成框架：在第一层，我们评估了这四个基本分类器的优势：支持向量机（SVM），随机森林（RF），K最近邻（KNN）和人工神经网络（ANN）；在第二层，应用逻辑回归（LR）分类器对PD进行分类。通过与传统集成模型进行比较来评估所提出模型的性能。

结果

所提方法的准确度为96.88％，准确度为100％，召回率为95％， $F_{\mathrm{1个}}$ PD和HC的得分分别为97.44％。

与现有方法的比较

分类结果表明，与传统的集成模型相比，该模型具有更好的性能。

结论

与每个传统模型相比，具有有效集成多个基本分类器的堆叠集成模型执行的准确性更高。在这项研究中开发的方法提供了一种新的策略来提高PD诊断和早期检测的准确性。